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Quadratic Gaussian Splatting: High Quality Surface Reconstruction with Second-order Geometric Primitives

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arxiv 2411.16392 v4 pith:SYZBF24M submitted 2024-11-25 cs.CV

classification cs.CV
keywords densitygeometricprimitivesplattingsurfacecurvaturedistancegaussian
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose Quadratic Gaussian Splatting (QGS), a novel representation that replaces static primitives with deformable quadric surfaces (e.g., ellipse, paraboloids) to capture intricate geometry. Unlike prior works that rely on Euclidean distance for primitive density modeling--a metric misaligned with surface geometry under deformation--QGS introduces geodesic distance-based density distributions. This innovation ensures that density weights adapt intrinsically to the primitive curvature, preserving consistency during shape changes (e.g., from planar disks to curved paraboloids). By solving geodesic distances in closed form on quadric surfaces, QGS enables surface-aware splatting, where a single primitive can represent complex curvature that previously required dozens of planar surfels, potentially reducing memory usage while maintaining efficient rendering via fast ray-quadric intersection. Experiments on DTU, Tanks and Temples, and MipNeRF360 datasets demonstrate state-of-the-art surface reconstruction, with QGS reducing geometric error (chamfer distance) by 33% over 2DGS and 27% over GOF on the DTU dataset. Crucially, QGS retains competitive appearance quality, bridging the gap between geometric precision and visual fidelity for applications like robotics and immersive reality.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Speed Always Wins: A Survey on Efficient Architectures for Large Language Models

    cs.CL 2025-08 conditional novelty 6.0 of 10

    RayletDF predicts ray-surface distances from learned raylet segment features and shows single-forward-pass 3D surface reconstruction that generalizes across unseen indoor datasets from point clouds or pre-fit 3D Gaussians.

  2. A Mixed-Primitive-based Gaussian Splatting Method for Surface Reconstruction

    cs.CV 2025-07 conditional novelty 6.0 of 10

    MP-GS combines Gaussian ellipses, line segments, and triangles as splatting primitives and reports state-of-the-art Chamfer distance on DTU and F1 on Tanks and Temples.

  3. Enhancing LLM Training via Spectral Clipping

    cs.LG 2026-03 unverdicted novelty 5.0 of 10

    SPECTRA improves LLM pretraining via post-clipping of update spectral norms and optional pre-clipping of gradient spikes, framed as Composite Frank-Wolfe regularization.

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